ICASSP 2023accepted0 citations
Provably Convergent Plug & Play Linearized ADMM, Applied to Deblurring Spatially Varying Kernels
Charles Laroche, Andrés Almansa, Eva Coupeté, Matias Tassano
Abstract
Plug & Play methods combine proximal algorithms with denoiser priors to solve inverse problems. These methods rely on the computability of the proximal operator of the data fidelity term. In this paper, we propose a Plug & Play framework based on linearized ADMM that allows us to bypass the computation of intractable proximal operators. We demonstrate the convergence of the algorithm and provide results on restoration tasks such as super-resolution and deblurring with non-uniform blur.
BibTeX
@inproceedings{icassp2023_provablyconverge,
title = {Provably Convergent Plug & Play Linearized ADMM, Applied to Deblurring Spatially Varying Kernels},
author = {Charles Laroche and Andrés Almansa and Eva Coupeté and Matias Tassano},
booktitle = {ICASSP 2023},
year = {2023}
}